An Integrated Approach to Optimization of WEDM Combining Single-Pass and Multipass Cutting Operation

Abstract

This research article presents an integrated approach to optimization of wire electrical discharge machining (WEDM) of gamma titanium aluminide (γ-TiAl) with the assistance of artificial neural network (ANN) modeling. Four process parameters, pulse on time, peak current, dielectric flow rate, and effective wire offset, were investigated to study their influence on the process outputs; that is, cutting speed, surface roughness, and dimensional shift in the multipass cutting operation. Two ANN models, based on Bayesian (automated) regularization and early stopping method, have been developed and compared. The model based on Bayesian regularization method was selected because the prediction accuracy was superior compared to the early stopping method. The Pareto optimization was applied to determine the maximum cutting speed corresponding to the required surface roughness for the trim cutting process. Finally, by combining the results of the single- and multipass cutting and introducing the new concept of effective cutting speed, a machining strategy based on the novel concept of critical surface roughness has been developed for selecting the machining process, either single cutting or multipass cutting, so that the maximum productivity can be ensured according to the surface finish requirements.